Fourier transform near-infrared (FT-NIR) spectroscopy is a nondestructive, rapid, real-time analysis of technical detection methods with an important reference value for producers and consumers. In this study, the feasibility of using FT-NIR spectroscopy for the rapid quantitative analysis and qualitative analysis of ‘Zaosu’ and ‘Dangshansuli’ pears is explored. The quantitative model was established by partial least squares (PLS) regression combined with cross-validation based on the spectral data of 340 pear fresh fruits and synchronized with the reference values determined by conventional assays. Furthermore, NIR spectroscopy combined with cluster analysis was used to identify varieties of ‘Zaosu’ and ‘Dangshansuli’. As a result, the model developed using FT-NIR spectroscopy gave the best results for the prediction models of soluble solid content (SSC) and titratable acidity (TA) of ‘Dangshansuli’ (residual prediction deviation, RPD: 3.272 and 2.239), which were better than those developed for ‘Zaosu’ SSC and TA modeling (RPD: 1.407 and 1.471). The results also showed that the variety identification of ‘Zaosu’ and ‘Dangshansuli’ could be carried out based on FT-NIR spectroscopy, and the discrimination accuracy was 100%. Overall, FT-NIR spectroscopy is a good tool for rapid and nondestructive analysis of the internal quality and variety identification of fresh pears.

Localizing the brain regions affected by tasks is crucial to understanding the mechanisms of brain function. However, traditional statistical analysis does not accurately identify the brain regions of interest due to factors such as sample size, task design, and statistical effects. Here, we propose a hypergraph-based multitask feature selection framework, referred to as HMTFS, which we apply to a functional magnetic resonance imaging (fMRI) dataset to extract task-related brain regions. HMTFS is characterized by its ability to construct a hypergraph through correlations between subjects, treating each subject as a node to preserve high-order information of time-varying signals. Additionally, it manages feature selection across different time windows in fMRI data as multiple tasks, facilitating time-constrained group sparse learning with a smoothness constraint. We utilize a large fMRI dataset from the Human Connectome Project (HCP) to validate the performance of HMTFS in feature selection. Experimental results demonstrate that brain regions selected by HMTFS can provide higher accuracy for downstream classification tasks compared to other competing feature selection methods and align with findings from previous neuroscience studies.